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$ \newcommand{\cclass}[1]{{\normalfont\textsf{##1}}} $We show average-case lower bounds for explicit Boolean functions against bounded-depth threshold circuits with a superlinear number of wires. We show that for each integer $d > 1$, there…

Computational Complexity · Computer Science 2018-06-19 Ruiwen Chen , Rahul Santhanam , Srikanth Srinivasan

We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective. In particular, we propose a geometric regularization technique to find the submanifold corresponding to a robust…

Machine Learning · Computer Science 2016-02-12 Qinxun Bai , Steven Rosenberg , Zheng Wu , Stan Sclaroff

In this paper we propose a generalization of deep neural networks called deep function machines (DFMs). DFMs act on vector spaces of arbitrary (possibly infinite) dimension and we show that a family of DFMs are invariant to the dimension of…

Machine Learning · Statistics 2017-11-08 William H. Guss

Let $[a,b]\subset\mathbb{R}$ be a non empty and non singleton closed interval and $P=\{a=x_0<\cdots<x_n=b\}$ is a partition of it. Then $f:I\to\mathbb{R}$ is said to be a function of $r$-bounded variation, if the expression…

General Mathematics · Mathematics 2023-06-07 Angshuman R. Goswami

A Boolean $k$-monotone function defined over a finite poset domain ${\cal D}$ alternates between the values $0$ and $1$ at most $k$ times on any ascending chain in ${\cal D}$. Therefore, $k$-monotone functions are natural generalizations of…

Data Structures and Algorithms · Computer Science 2016-09-15 Clément L. Canonne , Elena Grigorescu , Siyao Guo , Akash Kumar , Karl Wimmer

Let f:{-1,1}^n -> R be a real function on the hypercube, given by its discrete Fourier expansion, or, equivalently, represented as a multilinear polynomial. We say that it is Boolean if its image is in {-1,1}. We show that every function on…

Discrete Mathematics · Computer Science 2013-11-13 Tom Gur , Omer Tamuz

A boolean function $f(x_1,...,x_n)$ is \textit{weakly symmetric} if it is invariant under a transitive permutation group on its variables. A boolean function $f(x_1,...,x_n)$ is \textit{elusive} if we have to check all $x_1$,..., $x_n$ to…

Computational Complexity · Computer Science 2017-01-11 Guangmo Tong , Weili Wu , Ding-Zhu Du

This work proposes a unified theory of regularity in one hypercomplex variable: the theory of $T$-regular functions. In the special case of quaternion-valued functions of one quaternionic variable, this unified theory comprises…

Complex Variables · Mathematics 2026-04-10 Riccardo Ghiloni , Caterina Stoppato

We present a framework to calculate large deviations for nonlinear functions of independent random variables supported on compact sets in Banach spaces, by extending the result in Chatterjee and Dembo [6]. Previous research on nonlinear…

Probability · Mathematics 2018-07-12 Jun Yan

Kolmogorov-Arnold Networks (KANs) offer a promising framework for approximating complex nonlinear functions, yet the original B-spline formulation suffers from significant computational overhead due to De Boor algorithm. While recent…

Machine Learning · Computer Science 2026-02-10 Shao-Ting Chiu , Siu Wun Cheung , Ulisses Braga-Neto , Chak Shing Lee , Rui Peng Li

We investigate systematically dimension-9 operators in the standard model effective field theory which contains only standard model fields and respects its gauge symmetry. With the help of the Hilbert series approach to classifying…

High Energy Physics - Phenomenology · Physics 2020-11-30 Yi Liao , Xiao-Dong Ma

We study noisy computation in randomly generated k-ary Boolean formulas. We establish bounds on the noise level above which the results of computation by random formulas are not reliable. This bound is saturated by formulas constructed from…

Disordered Systems and Neural Networks · Physics 2014-11-21 Alexander Mozeika , David Saad

The renormalization group (RG) is an essential technique in statistical physics and quantum field theory, which considers scale-invariant properties of physical theories and how these theories' parameters change with scaling. Deep learning…

Statistical Mechanics · Physics 2023-08-23 Kelsie Taylor

In this paper we are concerned with the approximation of functions by single hidden layer neural networks with ReLU activation functions on the unit circle. In particular, we are interested in the case when the number of data-points exceeds…

Analysis of PDEs · Mathematics 2021-04-02 Benny Avelin , Vesa Julin

Non-linearity of a Boolean function indicates how far it is from any linear function. Despite there being several strong results about identifying a linear function and distinguishing one from a sufficiently non-linear function, we found a…

Quantum Physics · Physics 2021-12-28 Debajyoti Bera , Tharrmashastha Sapv

In this paper, several new classes of Boolean functions with few Walsh transform values, including bent, semi-bent and five-valued functions, are obtained by adding the product of two or three linear functions to some known bent…

Information Theory · Computer Science 2015-06-17 Guangkui Xu , Xiwang Cao , Shanding Xu

A Radial Basis Function Generated Finite-Differences (RBF-FD) inspired technique for evaluating definite integrals over the volume of the ball in three dimensions is described. Such methods are necessary in many areas of Applied…

Numerical Analysis · Mathematics 2020-06-11 Jonah A. Reeger

We deform the action and the supersymmetry transformations of the d=10 and d=4 Maxwell supermultiplets so that at each order of the deformation the theory has 16 Maxwell multiplet deformed supersymmetries as well as 16 Volkov-Akulov type…

High Energy Physics - Theory · Physics 2015-06-15 Eric Bergshoeff , Frederik Coomans , Renata Kallosh , C. S. Shahbazi , Antoine Van Proeyen

Fixed-order perturbative calculations for differential cross sections can suffer from non-physical artifacts: they can be non-positive, non-normalizable, and non-finite, none of which occur in experimental measurements. We propose a…

High Energy Physics - Phenomenology · Physics 2025-12-19 Rikab Gambhir , Radha Mastandrea

Autonomous spacecraft inspection and docking missions require controllers that can guarantee safety under thrust constraints and uncertainty. Input-constrained control barrier functions (ICCBFs) provide a framework for safety certification…

Systems and Control · Electrical Eng. & Systems 2026-02-10 Minduli C. Wijayatunga , Richard Linares , Roberto Armellin